Toward a systems-level view of dynamic phosphorylation networks

To better understand how cells sense and respond to their environment, it is important to understand the organization and regulation of the phosphorylation networks that underlie most cellular signal transduction pathways. These networks, which are composed of protein kinases, protein phosphatases and their respective cellular targets, are highly dynamic. Importantly, to achieve signaling specificity, phosphorylation networks must be regulated at several levels, including at the level of protein expression, substrate recognition, and spatiotemporal modulation of enzymatic activity. Here, we briefly summarize some of the traditional methods used to study the phosphorylation status of cellular proteins before focusing our attention on several recent technological advances, such as protein microarrays, quantitative mass spectrometry, and genetically-targetable fluorescent biosensors, that are offering new insights into the organization and regulation of cellular phosphorylation networks. Together, these approaches promise to lead to a systems-level view of dynamic phosphorylation networks

De novo sequencing of multiple tandem mass spectra of peptide containing SILAC labeling

The systematic studies of proteins has gradually become fundamental in the research related to molecular biology. Shotgun proteomics use bottom-up proteomics techniques in identifying proteins contained in complex mixtures using a combination of high performance liquid chromatography coupled with mass spectrometry technology. Current mass spectrometers equipped with high sensitivity and accuracy can produce thousands of tandem mass spectrometry (MS/MS) spectra in a single run. The large amount of data collected in a single LC-MS/MS run requires effective computational approaches to automate the process of spectra interpretation. De novo peptide sequencing from tandem mass spectrometry (MS/MS) has emerged as an important technology for peptide sequencing in proteomics. However, the low identification rate of the acquired mass spectral limits the efficiency of computational approaches. To increase the accuracy and practicality of de novo sequencing, some previous algorithms used multiple spectra to identify the peptide sequence. In this thesis, we focus on de novo sequencing of multiple SILAC labeled tandem mass spectra. Compared with previous approach, our research develop de novo sequencing algorithms based on different idea of how to use multiple spectra. SILAC technology uses medium containing different kinds of isotope-labeled essential amino acids, usually Arginine(R) and Lysine(K), to label newly synthesized proteins with stable isotopes during cell growth. Multiple MS/MS spectra for the same peptide sequence are produced by spectrometer after the SILAC samples are processed by LC-MS/MS shotgun proteomics. Based on the factors such as the type of isotope labeling, retention time, precursor ion mass, multiple spectra with different type of SILAC modifications for the same peptide in the sample can be used to identify the peptide sequence. In this study, not only are we aiming to identify the peptide sequence with specific SILAC modifications, but we are also pinpointing locations of SILAC modifications from multiple SILAC labeled MS/MS spectra. We propose two de novo sequencing algorithms to compute the peptide sequence which are based on total number of SILAC modifications and based on the combinations of SILAC modifications of Arginine(R) and Lysine(K). With two dynamic programming algorithms to identify peptide sequence and locating its SILAC modifications, the potential candidates are computed with similarity scores and then refinement algorithms are applied. Finally, a confident score is designed to measure all of the candidate sequence. To verify the performance of our algorithm, we compare the experimental results. We also compare the output candidates between our approach and PEAKS de novo

Complex Proteoform Identification Using Top-Down Mass Spectrometry

Indiana University-Purdue University Indianapolis (IUPUI)Proteoforms are distinct protein molecule forms created by variations in genes, gene expression, and other biological processes. Many proteoforms contain multiple primary structural alterations, including amino acid substitutions, terminal truncations, and posttranslational modifications. These primary structural alterations play a crucial role in determining protein functions: proteoforms from the same protein with different alterations may exhibit different functional behaviors. Because top-down mass spectrometry directly analyzes intact proteoforms and provides complete sequence information of proteoforms, it has become the method of choice for the identification of complex proteoforms. Although instruments and experimental protocols for top-down mass spectrometry have been advancing rapidly in the past several years, many computational problems in this area remain unsolved, and the development of software tools for analyzing such data is still at its very early stage. In this dissertation, we propose several novel algorithms for challenging computational problems in proteoform identification by top-down mass spectrometry. First, we present two approximate spectrum-based protein sequence filtering algorithms that quickly find a small number of candidate proteins from a large proteome database for a query mass spectrum. Second, we describe mass graph-based alignment algorithms that efficiently identify proteoforms with variable post-translational modifications and/or terminal truncations. Third, we propose a Markov chain Monte Carlo method for estimating the statistical signi ficance of identified proteoform spectrum matches. They are the first efficient algorithms that take into account three types of alterations: variable post-translational modifications, unexpected alterations, and terminal truncations in proteoform identification. As a result, they are more sensitive and powerful than other existing methods that consider only one or two of the three types of alterations. All the proposed algorithms have been incorporated into TopMG, a complete software pipeline for complex proteoform identification. Experimental results showed that TopMG significantly increases the number of identifications than other existing methods in proteome-level top-down mass spectrometry studies. TopMG will facilitate the applications of top-down mass spectrometry in many areas, such as the identification and quantification of clinically relevant proteoforms and the discovery of new proteoform biomarkers.2019-06-2

Complex Proteoform Identification Using Top-Down Mass Spectrometry

Indiana University-Purdue University Indianapolis (IUPUI)Proteoforms are distinct protein molecule forms created by variations in genes, gene expression, and other biological processes. Many proteoforms contain multiple primary structural alterations, including amino acid substitutions, terminal truncations, and posttranslational modifications. These primary structural alterations play a crucial role in determining protein functions: proteoforms from the same protein with different alterations may exhibit different functional behaviors. Because top-down mass spectrometry directly analyzes intact proteoforms and provides complete sequence information of proteoforms, it has become the method of choice for the identification of complex proteoforms. Although instruments and experimental protocols for top-down mass spectrometry have been advancing rapidly in the past several years, many computational problems in this area remain unsolved, and the development of software tools for analyzing such data is still at its very early stage. In this dissertation, we propose several novel algorithms for challenging computational problems in proteoform identification by top-down mass spectrometry. First, we present two approximate spectrum-based protein sequence filtering algorithms that quickly find a small number of candidate proteins from a large proteome database for a query mass spectrum. Second, we describe mass graph-based alignment algorithms that efficiently identify proteoforms with variable post-translational modifications and/or terminal truncations. Third, we propose a Markov chain Monte Carlo method for estimating the statistical signi ficance of identified proteoform spectrum matches. They are the first efficient algorithms that take into account three types of alterations: variable post-translational modifications, unexpected alterations, and terminal truncations in proteoform identification. As a result, they are more sensitive and powerful than other existing methods that consider only one or two of the three types of alterations. All the proposed algorithms have been incorporated into TopMG, a complete software pipeline for complex proteoform identification. Experimental results showed that TopMG significantly increases the number of identifications than other existing methods in proteome-level top-down mass spectrometry studies. TopMG will facilitate the applications of top-down mass spectrometry in many areas, such as the identification and quantification of clinically relevant proteoforms and the discovery of new proteoform biomarkers.2019-06-2

Algorithms for Characterizing Peptides and Glycopeptides with Mass Spectrometry

The emergence of tandem mass spectrometry (MS/MS) technology has significantly accelerated protein identification and quantification in proteomics. It enables high-throughput analysis of proteins and their quantities in a complex protein mixture. A mass spectrometer can easily and rapidly generate large volumes of mass spectral data for a biological sample. This bulk of data makes manual interpretation impossible and has also brought numerous challenges in automated data analysis. Algorithmic solutions have been proposed and provide indispensable analytical support in current proteomic experiments. However, new algorithms are still needed to either improve result accuracy or provide additional data analysis capabilities for both protein identification and quantification. Accurate identification of proteins in a sample is the preliminary requirement of a proteomic study. In many cases, a mass spectrum cannot provide complete information to identify the peptide without ambiguity because of the inefficiency of the peptide fragmentation technique and the prevalent existence of noise. We propose ADEPTS to this problem using the complementary information provided in different types of mass spectra. Meanwhile, the occurrence of posttranslational modifications (PTMs) on proteins is another major issue that prevents the interpretation of a large portion of spectra. Using current software tools, users have to specify possible PTMs in advance. However, the number of possible PTMs has to be limited since specifying more PTMs to the software leads to a longer running time and lower result accuracy. Thus, we develop DeNovoPTM and PeaksPTM to provide efficient and accurate solutions. Glycosylation is one of the most frequently observed PTMs in proteomics. It plays important roles in many disease processes and thus has attracted growing research interest. However, lack of algorithms that can identify intact glycopeptides has become the major obstacle that hinders glycoprotein studies. We propose a novel algorithm, GlycoMaster DB, to fulfil this urgent requirement. Additional research is presented on protein quantification, which studies the changes of protein quantity by comparing two or more mass spectral datasets. A crucial problem in the quantification is to correct the retention time distortions between different datasets. Heuristic solutions from previous research have been used in practice but none of them has yet claimed a clear optimization goal. To address this issue, we propose a combinatorial model and practical algorithms for this problem

Methodenentwicklung in der Qualitativen, Quantitativen und Computergestützten Proteomforschung

Protein phosphorylation is an important posttranslational modification that plays a regulatory role within numerous biological processes. The simultaneous identification, localization, and quantification of phosphorylated proteins is vital for understanding this dynamic control mechanism. The application of isobaric labeling strategies, e.g., iTRAQ, for quantitative phosphopeptide analysis requires (i) optimal peptide fragmentation conditions, (ii) sophisticated computational proteomics algorithms to identify (phosphorylated) iTRAQ labeled peptides, and (iii) the ability to use more than one Peptide-Spectra-Match and phosphopeptide sequence to guarantee accurate phosphorylation specific quantification. These three demands were combined into a platform to relatively quantify iTRAQ-4Plex labeled (phospho)proteins on a LTQ Orbitrap Velos.Die Protein Phosphorylierung stellt eine wichtige posttranslationale Modifikation dar, die eine Vielzahl biologischer Prozesse reguliert. Um die dynamischen Kontrollmechanismen besser verstehen zu können, ist es wichtig phosphorylierte Proteine identifizieren und quantifizieren zu können. Die Anwendung isobarer Derivatisierungsstrategien (z.B. iTRAQ) zur quantitativen Phosphopeptid-Analyse erfordert (i) optimal eingestellte Peptid-Fragmentierungsbedingungen und (ii) speziell angepasste Algorithmen zur Identifizierung iTRAQ derivatisierter (phosphorylierter) Peptide. Weiterhin (iii) wird eine Vielzahl an Peptide-Spectrum-Matches bzw. Phosphopeptid spezifischer Sequenzen für eine akkurate Quantifizierung benötigt. Diese drei Anforderungen wurden in einer Plattform vereinigt, um iTRAQ derivatisierte (Phospho)Proteine mittels massenspektrometrischer Analyse auf einer LTQ Orbitrap Velos relativ zueinander quantifizieren zu können